Imagine a virus that hits almost every toddler and sometimes leaves a permanent 'scar' on their lungs, leading to lifelong asthma. This work acts like a detective, using AI to find the specific genetic triggers and viral strains that cause this damage. Once they find the 'broken' switch in the cell, they search for existing drug-like molecules that can flip it back to normal.
Current medicine cannot predict which infants infected by RSV will develop lifelong asthma, and there are few targeted drugs to prevent this transition.
An AI-driven pipeline to identify genetic risk factors and drug-like compounds, validated using patient-derived airway organoids.
If you are a drug discovery firm dealing with a lack of targets for pediatric respiratory diseases — this project developed AI-based perturbation signatures that identify drug-like compounds capable of reversing RSV effects.
If you are a precision medicine lab dealing with the inability to predict which infants will develop chronic illness — this project developed a genetic risk score for long-term asthma development to enable personalized prevention.
If you are a biotechnology company dealing with low-accuracy drug screening — this project developed patient-derived airway organoid models to validate mechanisms and candidate compounds.
Based on available project data, there is no pricing information provided for the resulting risk scores or compounds.
The project focuses on identifying drug-like compounds and genetic scores; however, industrial scale-up data is not yet available in the current reports.
Based on available project data, the IP status is not specified, though the project involves 9 partners across 4 countries.
The project period runs from 2024-01-01 to 2028-12-31, suggesting a multi-year development cycle before clinical application.
The project uses AI to integrate new data with current biological knowledge to identify RSV-induced perturbation signatures.
The consortium is heavily research-oriented, consisting of 9 partners from 4 countries (DE, EE, ES, NL). It is dominated by 4 research organizations, 2 universities, and 3 other entities, with only 1 SME and 0% industry representation. This suggests the project is currently in a high-science discovery phase rather than a commercial deployment phase.
Universitair Medisch Centrum Utrecht
Contact us to track the transition of these AI-identified compounds from organoid validation to clinical trials.
